Aesthetic biases and opacity tactics in the training of visual artificial intelligence models

Abstract

This paper delves into the concept of taste as a classifier and its role in perpetuating cultural capital. Pierre Bourdieu’s study on class and taste highlights how those with higher cultural capital dictate societal notions of good taste, influencing dominated classes. In the era of machine learning visual generative methods, the perpetuation of cultural capital ownership occurs through statistical processes. The evaluation of visual neural networks’ aesthetic quality involves two crucial steps: filtering out low-quality images and creating test synthetic images for evaluation during training. However, these evaluations are biased, reflecting the preferences of a select group of individuals. Aesthetic evaluation data is obtained through public rating systems, but most of their users belong to a specific demographic, leading to further homogeneity in taste. Consequently, current rating systems reinforce a limited cultural capital rooted in access to technology and computational creativity. To foster diversity in neural generative systems, the paper proposes the development of new scorer systems, incorporating ratings from individuals in the Global South, those unfamiliar with generative systems or computers, and marginalized cultures. This endeavor aims to build a more inclusive aesthetic guidance and address the dominance of specific cultural capital in the field of visual culture. Finally, we also describe Edouard Glissant’s concept of opacity as a valid resistance strategy for cultures which do not want to be mapped within generative artificial intelligence models.